Electrocatalysts and photocatalysts are key to a sustainable future, generating clean fuels,
reducing the impact of global warming, and providing solutions to environmental pollution …

Heusler alloys, discovered serendipitously at the beginning of the twentieth century, have
emerged in the twenty-first century as exciting materials for numerous remarkable functional …

One of the most exciting tools that have entered the material science toolbox in recent years
is machine learning. This collection of statistical methods has already proved to be capable …

Recent advances in experimental and computational methods are increasing the quantity
and complexity of generated data. This massive amount of raw data needs to be stored and …

In recent years, we have been witnessing a paradigm shift in computational materials
science. In fact, traditional methods, mostly developed in the second half of the XXth century …

Advances in machine learning have impacted myriad areas of materials science, such as
the discovery of novel materials and the improvement of molecular simulations, with likely …

The materials discovery problem usually aims to identify novel “outlier” materials with
extremely low or high property values outside of the scope of all known materials. It can be …

The recently proposed crystal graph convolutional neural network (CGCNN) offers a highly
versatile and accurate machine learning (ML) framework by learning material properties …

Zeolites are porous, aluminosilicate materials with many industrial and “green” applications.
Despite their industrial relevance, many aspects of zeolite synthesis remain poorly …

Crystal‐graph attention neural networks have emerged recently as remarkable tools for the
prediction of thermodynamic stability. The efficacy of their learning capabilities and their …